International Journal of Crashworthiness

Short-term passenger flow forecasting is the key to implement real-time dynamic dispatching of buses, which can meet the travel time requirement of passengers with different attributes. In practice, it is difficult to obtain passenger attribute information due to the restriction of bus information systems or other conditions. This article proposes a new perspective on identifying passenger attribute information, that is, the correlation between the bus card number and the travel time is used to analyse passenger travel behaviour. Then using the travel frequency as the splitting boundary, the passenger set is split into different types of subsets, which are predicted by different methods. The total forecast values are obtained by integration, so as to explore the effectiveness of the passenger attribute identification and splitting–integrating method. The result shows that: (1) compared with the forecasting method without considering the passenger travel behaviour, the performance of splitting–integrating method is better, and the passenger attribute identification method is effective; (2) the value of the splitting boundary will affect the size and consistency of the subset, and the optimal value can be sought according to forecast results; (3) different types of subsets should be treated by different forecasting models and combination paths.

In the last decade, the causes of Greenhouse Gas (GHG) emissions were widely studied, to delete or, at least, mitigate them. In the road context, as reasonable, greater importance was assigned to the vehicles, since huge traffic flows, including high percentages of trucks, determine negative impacts on the environment. On the contrary, the role of the road infrastructure has always been considered marginal. It was thought as a functional element on which the traffic flows move, without evaluating the role of its geometrical characteristics on exhaust gas emissions. The proposed research aims to verify whether some road features, related to its horizontal geometry, influence the carbon dioxide production of vehicles or, on the contrary, if it is not sensitive to the different geometrical compositions. A driving simulator gives the opportunity to calculate the emissions from fuel consumption data, in turn, calculated through the engine mapping of an ordinary vehicle. The proposed procedure may be easily applied to any road context and may represent a further checking element for the infrastructure efficiency, in terms of environmental impacts. The results, derived from a test phase in a simulated environment and obtained using 3 different one-way ANOVAs, allowed the authors to define some interesting conclusions. The trend of the carbon dioxide function depends on curve radius and lengths and on tangent length; therefore, an opportune alignment design can effectively contribute to control emission values. The experiments confirmed that designing a consistent road is fundamental, but this cannot be deduced by traditional literature models.

The article presents an approach to train traffic modelling that allows for the analysis of how railway networks respond to various disturbances, including increased traffic and disturbance events. It discusses different methods of reconfiguration actions in key points of the railway network, which helps reduce delay propagation in the transport system. The 1st part covers building simulation models, which include defining infrastructure, setting train routes, configuring rolling stock, and disturbance scenarios, enabling the analysis of various disruptive events. The simulations allow for testing disturbance scenarios with minimal downtime risk without interfering with the real-world environment. The study results identified key system parameters generating the largest delays, such as platform availability, signaling, and the number of block sections. Probability density distributions for event intervals and durations were analyzed. The Kolmogorov–Smirnov test was used to confirm the fit of empirical distributions with theoretical ones, which were then implemented in the model of railway line No 271, running from Wrocław to Żmigród (Poland). As part of the reconfiguration of this railway line, new platforms were added, the time required for route setting was reduced, and the number of block sections was increased. These actions significantly reduced average delays, improved line capacity, and enhanced the robustness of the railway transport system against disturbances. The reconfiguration effectively reduced delays in areas causing significant time exceedances above 359 s, which was recognized in the Polish railway network as critical.

Technological advances in transport of the 20th century include aeroplanes, human spaceflight and Moon landings, submarines, and magnetic levitation trains. In the fast-paced world of technology, the achievements of previous centuries are often forgotten. One of the examples of human ingenuity is the funicular railway. Its technology has hardly changed over the years. Using a simple pulley system, passengers and freight are still lifted steep slopes with minimal energy consumption. This demonstrates the lasting value of simple solutions and engineering intelligence. The article discusses the world’s funiculars and their design characteristics. A correlation analysis for the number of funiculars per country (N) and the country mountainousness index (M) has been carried out. 3 types of regression models have been developed and their determination and correlation coefficients have been calculated. The highest correlation coefficient values are for quadratic and linear mathematical models. The critical values of the correlation coefficients were calculated and compared with the correlation coefficients obtained from the study. For all 36 countries, the correlation coefficient for N and M variables is above the critical value only when a quadratic regression model is used. The correlation coefficients for all models are above the critical value for the 15 economically advanced and tourism-developing countries. The study shows that the number of cable cars in economically developed countries is more strongly correlated with the degree of mountainousness of the country in question.

With the recognition of the impact of the transport sector on climate change and human health, decision-makers are under the pressure to shape the transport sector in a more sustainable way, considering more sustainable options and technologies. Besides that, it is also important to ensure such aspects as affordability, security, reliability and convenience of transport services and the effective functioning of the whole system. Therefore, transport-related policy actions require not only an economic point of view, but also environmental and social actions. The article aims to overview the application of Multi-Criteria Decision-Making (MCDM) techniques for solving sustainability issues in the transport sector and to provide the main insights for methods and sustainability criteria selection. The Search, Appraisal, Synthesis and Analysis (SALSA) framework and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement were applied as the basis for the research. The detailed content analysis of studies is arranged according to the application areas and the methods applied. In order to increase the applicability of the performed analysis and to simplify the decision-making for further studies, the thematic areas for criteria selection are proposed, the most popular MCDM techniques alongside their advantages and disadvantages are briefly discussed.

Dispatch centres are an important part of the feeder bus network, and their location affects the design process of the feeder route. In some remote areas with weak transport infrastructure, it is very important to find an effective tool to simultaneously select the optimal location of the dispatch centre as well as transit routing process, which could improve the performance of the feeder bus system. The purpose of this article is to present an integrated optimization model for joint location and dispatching decisions for Feeder Bus Route Design (FBRD). The proposed methodology can select a number of best dispatch centres in alternative sets and calculate the order of the demand points visited by the feeder route. The objective of the model is to simultaneously minimize the total construction cost of selected dispatch centres and the total operational cost of the designed feeder bus system. The methodology facilitates obtaining solutions using the design of an improved double population Bacterial Foraging Optimization (BFO) algorithm. For example, it redefines the solution coding and the heuristic used to randomly initialize the initial population. When applied to the design of a feeder bus system for a station at Nanjing (China), the results reveal that a reduced budget may lead to change in the location of the dispatch centre; a more distant centre is required, which may increase the total mileage cost of all feeder routes. A detailed comparison of the improved and standard BFO and CPLEX shows that the difference between solutions is acceptable. However, the calculation time is greatly reduced, thus proving the effectiveness of the proposed algorithm.

Drivers with aggressive driving style driving electric city buses with rapid response and high acceleration performance characteristics are more prone to have traffic accidents in the starting stage. It is of great importance to accurately identify the drivers with aggressive driving style for preventing traffic accidents of city buses. In this article, a starting driving style recognition method of electric city bus is firstly proposed based on deep learning with in-vehicle Controller Area Network (CAN) bus data. The proposed model can automatically extract the deep spatiotemporal features of multi-channel time series data and achieve end-to-end data processing with higher accuracy and generalization ability. The sample data set of driving style is established by pre-processing the collected in-vehicle CAN bus data including the status of driving and vehicle motion, the data pre-processing method includes data cleaning, normalization and sample segmentation. Data set is labelled with subjective evaluation method. The starting driving style recognition method based on Convolutional Neural Network (CNN) model is constructed. Multiple sets of convolutional layers and pooling layers are used to automatically extract the spatiotemporal characteristics of starting driving style hidden in the data such as velocity and pedal position etc. The fully connected neural network and incentive function Softmax are applied to establish the relationship mapping between driving data characteristics and the starting driving styles, which are categorized as cautious, normal and aggressive. The results show that the proposed model can accurately recognize the starting driving style of electric city bus drivers with an accuracy of 98.3%. In addition, the impact of different model structures on model performance such as accuracy and F1 scores was discussed, and the performance of the proposed model was also compared with Support Vector Machine (SVM) and random forest model. The method can be used to accurately identify drivers with aggressive starting driving style and provide references for driver’s safety education, so as to prevent accidents at the starting stage of electric city bus and reduce crash accidents.

Short-term metro passenger flow prediction plays a great role in traffic planning and management, and it is an important prerequisite for achieving intelligent transportation. So, a novel hybrid Support Vector Regression (SVR) model based on Twice Clustering (TC) is proposed for short-term metro passenger flow prediction. The training sets and test sets are generated by TC with respect to values of passenger flow in different time periods to improve the prediction accuracy. Furthermore, each obtained cluster is decomposed by using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm and the Ensemble Empirical Mode Decomposition (EEMD) algorithm, respectively. The volatility of each component obtained after decomposition is further reduced. Then, the SVR model optimized by the Grey Wolf Optimization (GWO) algorithm is used to predict the decomposed components. Moreover, forecast based on one-month data from Xi’an Metro Line 2 Library Station (China). By comparing the prediction results of the TC condition, the Once Clustering (OC) condition and the non-clustering condition, it shows that the TC approach can adequately model the volatility and effectively improve the prediction accuracy. At the same time, experimental results show that the novel hybrid TC–CEEMDAN–GWO–SVR model has superior performance than Genetic Algorithm (GA) optimized SVR (SVR–GA) model and hybrid Back Propagation Neural Network (BPNN) model.

Encouraging students to commute by the metro can effectively reduce household car use caused by long-distance commuting to school. This article focuses on the frequency of metro use by groups of students commuting to school based on the assumption that students who use the metro may occasionally be driven to school by their parents. For the 1st time, we propose a school metro commuter identification process that considers the potential behaviour of escorted students, and we study the potential car trips for long-distance school escorting in Nanjing (China) using Smart Card Data (SCD) and a household travel survey from Nanjing. 3 clusters of students who use the metro for commutes to school are identified by frequency of use for possible escorting behaviour based on the commuting day. As possible factors influencing the 3 frequency groups, usage pattern of the metro, entry time, travel duration and the school–housing relationship are extracted from SCD. Furthermore, a multinomial logistic regression model is used to examine the significant factors that influence the grouping of students. The results show that students who use the metro occasionally for a long commuting distance to school are more likely to be escorted to and from school by their parents, especially to school. The later the entry time is to the metro, the more likely that students are to be escorted to school. Additionally, a long school–housing travel duration/distance significantly contributes to parents’ car trips for commuting. The results of this article are valuable for transport policy to reduce car use for long-distance school trips.

The aim of the article is to analyse the relationship between company growth, measured as an increase in Earnings Per Share (EPS) in 3- and 5-year periods, and companies’ financial condition, measured using the Altman z-Score (AS) model. The study was carried out on the example of companies included in the WIG Index and Warsaw Stock Exchange Index (in Polish: Warszawski Indeks Giełdowy – WIG) Environmental, Social, and Governance (ESG) between 2013 and 2020. Furthermore, among the companies included in the WIG index, companies belonging to the logistics industry were distinguished. An analysis of linear and panel relationships was used to verify the nature of the relationships between the variables taken into account. The z-Altman index was found to have a positive effect on company growth in a 3-year period for companies from the transport and logistics industry and all companies included in the WIG ESG index. Regarding company growth over the longer 5-year period, the influence of the z-Altman index on growth was not observed. Therefore, the results for companies in the WIG index show that for company growth in both the 3- and 5-year periods, the financial and economic condition of a company, measured by the z-Altman index, has no impact on the size of this growth, which was also confirmed by panel models.

The article provides statistics on failures of rubber-cord couplings of electric trains of the ER2 and ER2T series and of the diesel trains over the past 7 years. According to statistics, over the past 7 years, 107 rubber-cord couplings have failed. Of these, the largest number of cases of failure of rubber-cord couplings occurred on rolling stock of the ER2 series. Examining failed rubber-cord couplings, it was revealed that the cause of its failure was a rupture of the side surface. Replacing a rubber-cord coupling is a labour-intensive and costly process. Accordingly, the question arises: what causes the problem and what measures should be proposed to reduce the failures. For these purposes, the work presents a number of experiments in order to identify possible causes of failure of the rubber-cord coupling. The article presents studies of the heating temperature of rubber-cord couplings in operation on motor cars, as well as a number of studies of failed rubber-cord couplings removed from motor cars. During the research, such parameters as the date of the last repair and the date of failure of the rubber-cord coupling were taken into account. The number of days the motor car was in general operation was taken into account until the failure of the rubber-cord coupling, as well as the mileage of the motor car after the repair. Measurements were carried out of the geometric parameters of the rubber-cord coupling: outer and inner diameter, thickness of the side of the rubber-cord coupling. The torque of the rubber-cord coupling acting at speeds from 5 to 40 km/h, the forces acting in operation on the rubber-cord coupling were calculated, and torsional and shear stresses were also studied and determined. Research was carried out to determine the hardness of the rubber-cord coupling in the temperature range from –20 °C to 0 °C and from 0 °C to +22 °C, as well as from +22 °C to +60 °C. These parameters were taken since a rubber-cord coupling operates under the mentioned conditions. In conclusion, possible reasons for the failure of rubber-cord couplings are given, and recommendations for reduction of their frequency are proposed.

Maintaining desired subgrade performance is an effective way to reduce runway pavement deterioration. Due to lack of extensive field test data, life-cycle performance of runway subgrade has not been fully understood. In order to quantitatively estimate subgrade condition, a novel method of evaluating subgrade performance was developed and validated using the 726 sets of Heavy Weight Deflectometer (HWD) test data of ten runway sections. Statistical analysis demonstrates that the structural behaviour of rigid runway subgrade follows normal distribution in different service stages and can be efficiently evaluated by the subgrade performance index (ψ). The results of factor analysis show that Accumulated Air Traffic Volume (ATV) during service life is the major cause of spatial variations in subgrade condition. In the designed service period of runway, it validates that sea-reclaimed subgrade results in faster degradation in the initial stage of service life while thicker pavement exhibits better capability in protecting the subgrade soil in long-term view. Besides, the differences in applied loads and pavement thickness give rise to the subgrade performance variation in longitudinal direction. Meanwhile, the comparison between the main and the less trafficked test lines in transversal direction reveals that the aircraft impacts play a positive role in resisting the natural fatigue process. By the suggested method, subgrade performance of HWD test points can be categorized into 4 levels from “Excellent”, “Good”, “Fair” to “Poor” based on ψ value. It is helpful for airport agency to make scientific decisions on Maintenance and Rehabilitation (M&R) treatment by calculating the effective area of envelope (β) using the ratio of subgrade performance (η).

The Container Relocation Problem (CRP) is one of the most important optimization problems in maritime container terminals. The objective is to minimize the number of relocation operations for retrieving containers in a sequence. If the container to be retrieved next is not at the top of a stack, unproductive relocations have to be carried out. Due to the large number of containers handled by busy terminals, a slight reduction in relocation rates can result in significant savings in operating costs. Most of the existing heuristics make relocation decisions for the blocking containers one by one, based on simple indicators. In this article, we propose a Three-Stage Heuristic (3SH) that extends the decision horizon to multiple containers to achieve a higher-quality solution. Computational experiments are conducted on 3 sets of benchmark instances, and the results show that the proposed heuristic outperforms the state-of-the-art heuristics documented in the research literature.

The shift towards sustainable transportation is becoming increasingly important as the negative impact of traditional fuel-powered vehicles on the environment becomes more evident. Electric Vehicles (EVs) are considered a viable solution to this problem, and understanding the factors that influence consumer intention to purchase EVs is crucial for their widespread adoption. This study investigates the factors that influence individuals’ intention to purchase EVs. 4 independent variables were considered: Perceived Environmental Benefit (PEB), Perceived Performance Benefit (PPB), Perceived Performance Risk (PPR), and Perceived Financial Risk (PFR). A survey was conducted with 398 respondents, and the data collected were analysed using Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modelling (SEM). The results indicate that PEB, PPB, PPR, and PFR have significant effects on Purchase Intention (PI). Specifically, PEB and PPB positively affect PI, while PPR and PFR negatively affect it. These findings suggest that improving the PEBs and PPBs of EVs and reducing perceived performance and financial risks could encourage more individuals to purchase them.

This study aimed to determine the level of infrastructure-based rail freight accessibility and rail freight performance of several Central and Eastern European (CEE) countries in the context of their presence in the Eurasian rail freight transport system. The study′s object was 7 CEE countries: Estonia, Lithuania, Latvia, Poland, Czechia, Slovakia and Hungary. The research methodology was based on the TOPSIS method supplemented with literature and statistical analyses. Several selected numerical indicators were considered to create 2 rankings that displayed the results achieved by countries in terms of accessibility and performance. Results showed that Czechia is the leader in infrastructure-based accessibility, with Latvia closing the ranking, and Lithuania is the leader in rail freight performance, with Hungary closing the ranking. Even though the study did not allow to confirm that a country′s rail freight accessibility affects its rail freight performance and vice versa, it can be assumed that both parameters are crucial in the context of the incoming modal shift to rail freight in Eurasia; therefore, they constitute a valuable research endeavour.